We propose a novel iterative M-MIMO receiver referred to as linear Bayesian learning (LBL) receiver to cater for higher reliability and lower latency requirements in URLLC traffic. We first build the system model wherein an M-MIMO receiver at the base station is used to detect the symbols sent by multiple users. We assume that the channel estimates for different users are available and have been calculated by using different processes at the base stations. The developed LBL receiver consists of three modules; Bayesian symbol observation (BSO), Bayesian symbol estimate (BSE) and DSC modules. In the BSO module, we apply the MRC scheme to the received signals in order to get the symbol estimates, referred to as the observed symbols. For each observed symbol, PIC scheme is then used to remove its interference. The symbols variance is also calculated.
In the BSE module, the observed symbols and their variances are then utilised to construct maximum likelihood Gaussian distribution functions. The soft symbol estimates are then calculated based on the likelihood functions. These estimates are used to compute symbol error between estimations and observations. In the DSC module, the value of symbol errors in current and previous iterations are used to calculate the symbol estimates.
The process is then repeated iteratively and finally the DSC outputs are taken as the symbol estimates. The simulation results show that the
BER and latency performances of the proposed LBL receiver outperform existing classical and Bayesian M-MIMO receivers in [1]}, [2]}, [3]} by minimum 2 dB and 19 times for various M-MIMO system configurations.
The main contributions of this paper are as follows
